Table Structure Recognition with Conditional Attention

by   Bin Xiao, et al.

Tabular data in digital documents is widely used to express compact and important information for readers. However, it is challenging to parse tables from unstructured digital documents, such as PDFs and images, into machine-readable format because of the complexity of table structures and the missing of meta-information. Table Structure Recognition (TSR) problem aims to recognize the structure of a table and transform the unstructured tables into a structured and machine-readable format so that the tabular data can be further analysed by the down-stream tasks, such as semantic modeling and information retrieval. In this study, we hypothesize that a complicated table structure can be represented by a graph whose vertices and edges represent the cells and association between cells, respectively. Then we define the table structure recognition problem as a cell association classification problem and propose a conditional attention network (CATT-Net). The experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods on various datasets. Besides, we investigate whether the alignment of a cell bounding box or a text-focused approach has more impact on the model performance. Due to the lack of public dataset annotations based on these two approaches, we further annotate the ICDAR2013 dataset providing both types of bounding boxes, which can be a new benchmark dataset for evaluating the methods in this field. Experimental results show that the alignment of a cell bounding box can help improve the Micro-averaged F1 score from 0.915 to 0.963, and the Macro-average F1 score from 0.787 to 0.923.


page 3

page 5

page 7

page 10

page 12


Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling

Table structure recognition aims to extract the logical and physical str...

Image-based table recognition: data, model, and evaluation

Important information that relates to a specific topic in a document is ...

Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context

Documents are often the format of choice for knowledge sharing and prese...

Handling big tabular data of ICT supply chains: a multi-task, machine-interpretable approach

Due to the characteristics of Information and Communications Technology ...

LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment

Table structure recognition is a challenging task due to the various str...

GFTE: Graph-based Financial Table Extraction

Tabular data is a crucial form of information expression, which can orga...

DocParser: Hierarchical Structure Parsing of Document Renderings

Translating document renderings (e.g. PDFs, scans) into hierarchical str...

Please sign up or login with your details

Forgot password? Click here to reset